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DMG MORI Redesigns Robotic End-Effector using Topology Optimization & Reusable Workflows
技术
- 分析与建模 - Generative AI
- 分析与建模 - 预测分析
- 功能应用 - 制造执行系统 (MES)
适用行业
- 航天
- 汽车
- 消费品
适用功能
- 产品研发
用例
- 工厂可见化与智能化
- 自动化制造系统
- 预测性维护
服务
- 软件设计与工程服务
- 系统集成
挑战
ADDITIVE INTELLIGENCE, DMG MORI’s additive manufacturing design consultancy, was tasked with maximizing the stiffness-to-weight ratio of the head of the robotic end-effector while improving handling precision and reducing manufacturing costs. A key design requirement was to keep the external form factor of the component unaltered. At the same time, the robot head had to house the embedded channels of the pneumatic system and the end effector’s electrical components.
关于客户
DMG MORI is a leader in metal-cutting manufacturing equipment, producing high-quality CNC machines for over a century. The Robo2Go system is integral to the company’s factory automation offering. ADDITIVE INTELLIGENCE, DMG MORI’s additive manufacturing design consultancy, was tasked with maximizing the stiffness-to-weight ratio of the head of the robotic end-effector while improving handling precision and reducing manufacturing costs. A key design requirement was to keep the external form factor of the component unaltered. At the same time, the robot head had to house the embedded channels of the pneumatic system and the end effector’s electrical components.
解决方案
Since the engineers could not alter the external form of the part, the results of topology optimization could not be used directly as the final design. However, they could be used to vary the thickness of the outer shell. This process helped the team to grasp some of the structural benefits of topology optimization without changing the part’s exterior. The engineers of DMG MORI filled the shell with a conformal lattice to increase the stiffness of the part and create a permanent support structure for additive manufacturing. The team used nTopology’s engineering simulation capabilities to rapidly iterate between the available options and select a suitable lattice design. Instead of creating a one-off design, the engineers of DMG MORI developed a robust and reusable optimization process. Using as input the color-coded surfaces of each subsystem of the imported CAD file, the nTop workflow that the team developed can automatically rerun — even if the geometry changes due to design iterations or future projects.
运营影响
数量效益
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